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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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4897145193 · Jun 202019922001200920172026
48 results for power decay

Optimal learning rates decay to zero in easy tasks and maintain a warmup phase in hard tasks.

problem Optimizing learning rates under functional scaling laws for model training.
method Deriving optimal learning-rate schedules based on exponents ss and ββ.
result Sharp phase transition between easy and hard tasks, with different decay behaviors.

The probability of default (PD) estimation is an important process for financial institutions. The difficulty of the estimation depends on the correlations between borrowers. In this paper, we introduce a hierarchical Bayesian estimation method using the beta binomial distribution and consider a multi-year case with a …

2019-02-11abs ↗pdf ↗

The paper predicts and explains the decay of stock anomaly performance over time.

problem Predicting and explaining the drop in risk-adjusted performance of stock anomalies.
method The authors propose ex-ante characteristics based on hypotheses of out-of-sample decay and in-sample overfitting.
result The year of publication explains 30% of the variance in Sharpe decay across factors.

SignSGD outperforms SGD in linear regression with optimal scaling laws under PLRF model.

problem Improving linear regression performance with signSGD under power-law random features.
method Analysis of signSGD risk under PLRF model, comparison with SGD, identification of unique effects.
result SignSGD can have a steeper compute-optimal slope than SGD in noisy regimes, especially with WSD schedule.

Approximations to utility indifference prices are provided for a contingent claim in the large position size limit. Results are valid for general utility functions on the real line and semi-martingale models. It is shown that as the position size approaches infinity, the utility function's decay rate for large negative…

2012-02-17abs ↗pdf ↗

The paper presents a multi-power law for predicting loss curves across different learning rate schedules.

problem Understanding and optimizing the relationship between model performance and hyperparameters, especially learning rates.
method Proposes a multi-power law that combines power laws based on the sum of learning rates and additional laws for loss reduction due to decay.
result The multi-power law accurately predicts loss curves for unseen learning rate schedules and finds a schedule that outperforms cosine learning rate.

The paper studies harmonic map heat flow stability and decay rates.

problem Analyzing stability and decay rates of harmonic map heat flow solutions.
method Use of homogeneous Besov space B˙p,dp(Rd)\dot{B}^{\frac{d}{p}}_{p,\infty}(\mathbb{R}^d) for small initial data and self-similar decay assumption.
result Decay rates for solutions of the harmonic map flow of the form ablau(t)L(Rd)Ct12\| abla u(t) \|_{L^\infty(\mathbb{R}^d)}\leq Ct^{-\frac12} and self-similar decay under stronger initial conditions.

The persistence phenomenon is studied in the Japanese financial market by using a novel mapping of the time evolution of the values of shares quoted on the Nikkei Index onto Ising spins. The method is applied to historical end of day data from the Japanese stock market during 2002. By studying the time dependence of th…

2008-03-04abs ↗pdf ↗

Estimates self- and cross-impact concavity and decay patterns in financial markets.

problem Understanding the impact of financial transactions on market dynamics.
method Nonparametric estimation of concave multi-asset propagator models using metaorders and order flow data.
result Concave self-impact with shifted power-law decay, significant gain from cross-impact, and improved predictive accuracy.

The study uses the Merton model to estimate PD and finds a phase transition affecting convergence speed.

problem Estimating the probability of default (PD) using limited historical data.
method Adopted the Merton model and analyzed phase transitions in default correlation.
result PD estimation converges slowly when temporal correlation decays by power law less than one.

Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data. But, it is incapable of handling several complex features ubiquitously exhibited in real-world networks, one of which is the power-law degree characteristic. To this end, we propose a new var…

2019-04-05abs ↗pdf ↗

We compare systematically several classes of stochastic volatility models of stock market fluctuations. We show that the long-time return distribution is either Gaussian or develops a power-law tail, while the short-time return distribution has generically a stretched-exponential form, but can assume also an algebraic …

2010-09-14abs ↗pdf ↗

We explain theoretically a curious empirical phenomenon: "Approximating a matrix by deterministically selecting a subset of its columns with the corresponding largest leverage scores results in a good low-rank matrix surrogate". To obtain provable guarantees, previous work requires randomized sampling of the columns wi…

2014-04-06abs ↗pdf ↗

The study examines Kernel Ridge Regression error rates across noiseless and noisy conditions.

problem Characterizing Kernel Ridge Regression error rates in different noise levels.
method Unified analysis of Kernel Ridge Regression under various noise and regularization conditions.
result A crossover from noiseless to noisy error rates is observed as sample complexity increases.

We discover scaling laws for kernel regression loss under various learning rate schedules.

problem Understanding loss dynamics and learning rate schedules in kernel regression.
method Theoretical analysis of stochastic gradient descent on a power-law kernel regression model.
result Established a Functional Scaling Law (FSL) capturing the full loss trajectory under arbitrary learning rate schedules.

Optimizes trading strategy considering alpha decay and transaction costs.

problem Maximizing reward in a multi-period portfolio with transaction costs and alpha decay.
method Formulated as an infinite horizon Markov Decision Process, solved using a modified value iteration algorithm with convergence proof and asymptotic analysis.
result Characterized optimal trading policy that maximizes average expected reward.

We model the arrival of mid-price changes in the E-Mini S&P futures contract as a self-exciting Hawkes process. Using several estimation methods, we find that the Hawkes kernel is power-law with a decay exponent close to -1.15 at short times, less than approximately 10^3 seconds, and crosses over to a second power-law …

2013-02-06abs ↗pdf ↗

Second part of series studying charged scalar fields on Reissner--Nordström spacetimes.

problem Analyzing late-time behavior and stability of charged scalar fields on black hole backgrounds.
method Purely physical-space based methods, energy estimates, inverse-power laws.
result First pointwise decay estimates for charged scalar fields on black hole backgrounds.

Gradient descent outperforms ridge regression under certain covariance matrix decay conditions.

problem Comparing the performance of gradient descent and ridge regression in linear models.
method Investigated gradient descent and ridge regression for linear regression with random isotropic ground truth.
result Gradient descent outperforms ridge regression under specific covariance matrix decay conditions.

Power-law spectrum of random feature model is preserved in neural networks.

problem Preserving power-law spectrum in neural networks through random feature model.
method Characterized eigenvalues of population random-feature covariance using dyadic head-tail decomposition and Wick chaos expansions.
result Power-law exponent αα is inherited from input covariance, modified by a logarithmic correction.

In this paper, we describe a newly discovered statistical property of time series data for daily price changes. We conducted quantitative investigation of the {\it calm-time intervals} of price changes for 800 companies listed in the Tokyo Stock Exchange, and for the Nikkei 225 index over a 27-year period from January …

2003-12-21abs ↗pdf ↗

Using a proprietary dataset of meta-orders and prediction signals, and assuming a quasi-linear impact model, we deconvolve market impact from past correlated trades and a predictable return component to elicit the temporal dependence of the market impact of a single daily meta-order, over a ten day horizon in various e…

2014-07-12abs ↗pdf ↗

Analyzed Bitcoin market index volatility changes over two distinct periods using anomalous diffusion and multifractal analysis.

problem Characterizing volatility changes in Bitcoin market index over two distinct periods.
method Analyzed high-frequency Bitcoin data from 2019 to 2022, using anomalous diffusion and multifractal analysis.
result Volatility changes from subdiffusion to weak superdiffusion over time, with multifractal and self-similar properties.

Bayesian inference and superstatistics model financial volatility dynamics across different timescales.

problem Modeling correlated volatility in financial time series with heavy tails and long memory.
method Superstatistical dynamics, Bayesian Inference, Metropolis-Hasting sampling.
result The log-Normal model is reliable for short timescales, while inverse-Gamma is preferred for long timescales.

This article provides a novel framework to evaluate limit order tactics that highlights expected fill price, adverse price selection cost, and opportunity cost. We formulate the problem of optimal execution of market orders with nonlinear market impact, power law decay kernel, and stochastic and deterministic liquidity…

2014-09-04abs ↗pdf ↗